Name
Abstract | ||
|---|---|---|
Anomaly detection is the task of identifying unusual samples in data. Typically anomaly detection is defined on an unlabeled dataset that is assumed most of the samples are normal and others are anomalies. However, in industrial practice, one may have access to a part of annotated data. This gives us the potential for semi-supervised learning. In addition, existing methods assume all training data is normal and neglect the impact of a small number of anomalous samples. In this paper, we consolidate the model's discriminative power by introducing a transfer learning scheme to anomaly detection, thereby the model suffers less perturbation caused by pollution. We also propose a novel loss function to further adapt to semi-supervised data scenario. We ensure that the contribution of pollution can be well suppressed and reach a harmonious balance in magnitude of loss/gradient between unlabeled and labeled samples. Experiments on three publicly available datasets show that our method achieves state-of-the-art results. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414285 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Anomaly detection, semi-supervised learning, transfer learning, sample reweighting | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Feng | 1 | 0 | 0.34 |
| Jie Tang | 2 | 9 | 5.86 |
| Yishun Dou | 3 | 0 | 0.34 |
| Gang-Shan Wu | 4 | 27 | 6.75 |